{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  基础作业\n",
    "代码不作为评判标准，如果运⾏正确，则认为代码没有错误。  \n",
    "没有明显报错的正常的log输出 60分。  \n",
    "对模型结构的理解10分。  \n",
    "对模型训练过程（梯度如何计算，参数如何更新）的理解10分。  \n",
    "对计算图的理解10分。   \n",
    "解释这⾥的模型为什么效果⽐较差10分。    \n",
    "\n",
    "##  运行课程提供代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./train-images-idx3-ubyte.gz\n",
      "Extracting ./train-labels-idx1-ubyte.gz\n",
      "Extracting ./t10k-images-idx3-ubyte.gz\n",
      "Extracting ./t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# %matplotlib inline\n",
    "\n",
    "tf.logging.set_verbosity(tf.logging.INFO)\n",
    "mnist = input_data.read_data_sets(\"./\")\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "for idx in range(16):\n",
    "    plt.subplot(4, 4, idx + 1)\n",
    "    plt.axis('off')\n",
    "    plt.title('[{}]'.format(mnist.train.labels[idx]))\n",
    "    plt.imshow(mnist.train.images[idx].reshape((28, 28)))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 100 training steps, the loss is 0.60914, the validation accuracy is 0.875\n",
      "after 200 training steps, the loss is 0.394229, the validation accuracy is 0.8958\n",
      "after 300 training steps, the loss is 0.422044, the validation accuracy is 0.9164\n",
      "after 400 training steps, the loss is 0.507155, the validation accuracy is 0.9282\n",
      "after 500 training steps, the loss is 0.280971, the validation accuracy is 0.9324\n",
      "after 600 training steps, the loss is 0.087427, the validation accuracy is 0.9478\n",
      "WARNING:tensorflow:From /opt/anaconda3/envs/ai_1/lib/python3.7/site-packages/tensorflow/python/training/saver.py:960: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to delete files with this prefix.\n",
      "after 700 training steps, the loss is 0.207005, the validation accuracy is 0.9464\n",
      "after 800 training steps, the loss is 0.145555, the validation accuracy is 0.934\n",
      "after 900 training steps, the loss is 0.208377, the validation accuracy is 0.9422\n",
      "the training is finish!\n",
      "the test accuarcy is: 0.9508\n"
     ]
    }
   ],
   "source": [
    "x = tf.placeholder(\"float\", [None, 784])\n",
    "y = tf.placeholder(\"int64\", [None])\n",
    "learning_rate = tf.placeholder(\"float\")\n",
    "\n",
    "\n",
    "def initialize(shape, stddev=0.1):\n",
    "    return tf.truncated_normal(shape, stddev=0.1)\n",
    "\n",
    "\n",
    "L1_units_count = 100\n",
    "\n",
    "W_1 = tf.Variable(initialize([784, L1_units_count]))\n",
    "b_1 = tf.Variable(initialize([L1_units_count]))\n",
    "logits_1 = tf.matmul(x, W_1) + b_1\n",
    "output_1 = tf.nn.relu(logits_1)\n",
    "\n",
    "L2_units_count = 10\n",
    "W_2 = tf.Variable(initialize([L1_units_count, L2_units_count]))\n",
    "b_2 = tf.Variable(initialize([L2_units_count]))\n",
    "logits_2 = tf.matmul(output_1, W_2) + b_2\n",
    "\n",
    "logits = logits_2\n",
    "\n",
    "cross_entropy_loss = tf.reduce_mean(\n",
    "    tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(\n",
    "    learning_rate=learning_rate).minimize(cross_entropy_loss)\n",
    "\n",
    "pred = tf.nn.softmax(logits)\n",
    "correct_pred = tf.equal(tf.argmax(pred, 1), y)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "\n",
    "batch_size = 32\n",
    "trainig_step = 1000\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    # 定义验证集与测试集\n",
    "    validate_data = {\n",
    "        x: mnist.validation.images,\n",
    "        y: mnist.validation.labels,\n",
    "    }\n",
    "    test_data = {x: mnist.test.images, y: mnist.test.labels}\n",
    "\n",
    "    for i in range(trainig_step):\n",
    "        xs, ys = mnist.train.next_batch(batch_size)\n",
    "        _, loss = sess.run(\n",
    "            [optimizer, cross_entropy_loss],\n",
    "            feed_dict={\n",
    "                x: xs,\n",
    "                y: ys,\n",
    "                learning_rate: 0.3\n",
    "            })\n",
    "\n",
    "        # 每100次训练打印一次损失值与验证准确率\n",
    "        if i > 0 and i % 100 == 0:\n",
    "            validate_accuracy = sess.run(accuracy, feed_dict=validate_data)\n",
    "            print(\n",
    "                \"after %d training steps, the loss is %g, the validation accuracy is %g\"\n",
    "                % (i, loss, validate_accuracy))\n",
    "            saver.save(sess, './model_1/model_trained.ckpt', global_step=i)\n",
    "\n",
    "    print(\"the training is finish!\")\n",
    "    # 最终的测试准确率\n",
    "    acc = sess.run(accuracy, feed_dict=test_data)\n",
    "    print(\"the test accuarcy is:\", acc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/anaconda3/envs/ai_1/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from ./model_1/model_trained.ckpt-900\n",
      "0.96\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    ckpt = tf.train.get_checkpoint_state('./model_1')\n",
    "    if ckpt and ckpt.model_checkpoint_path:\n",
    "        saver.restore(sess, ckpt.model_checkpoint_path)\n",
    "        final_pred, acc = sess.run(\n",
    "            [pred, accuracy],\n",
    "            feed_dict={\n",
    "                x: mnist.test.images[:25],\n",
    "                y: mnist.test.labels[:25]\n",
    "            })\n",
    "        orders = np.argsort(final_pred)\n",
    "        plt.figure(figsize=(10, 10))\n",
    "        print(acc)\n",
    "        for idx in range(25):\n",
    "            order = orders[idx, :][-1]\n",
    "            prob = final_pred[idx, :][order]\n",
    "            plt.subplot(5, 5, idx + 1)\n",
    "            plt.axis('off')\n",
    "            plt.title('{}: [{}]-[{:.1f}%]'.format(mnist.test.labels[idx],\n",
    "                                                  order, prob * 100))\n",
    "            plt.imshow(mnist.test.images[idx].reshape((28, 28)))\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    else:\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对模型结构的理解\n",
    "\n",
    "该模型是一个训练对图片中的数字进行识别的模型，是一个全连接神经网络。  \n",
    "图片大小为28*28像素，转换为一个784维的向量，所以该模型的输入是一个784维的向量。  \n",
    "程序中定义了 L1_units_count = 100，L2_units_count = 10  \n",
    "这个模型的第一层有100个节点，计算结果用Relu函数进行激活，传到下一层。  \n",
    "输出层有10个节点，用softmax函数进行激活，从而得到预测的结果（数字0-9）。  "
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对模型训练过程（梯度如何计算，参数如何更新）的理解\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "在模型训练过程中，根据BP1公式，利用预测损失和激活函数的导数求出梯度。根据BP2公式，利用反向传播的方式计算每一层的梯度变化，再以此更新所有权重参数和偏置。   \n",
    "模型中使用交叉熵损失函数，利用GradientDescentOptimizer往梯度方向优化，在每个step中更新权重和偏置参数，使得损失逐渐减少。  \n",
    "经过1000个step的训练，得到准确率较高的模型，最后对测试数据进行预测，得到0.9508的准确率。  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对计算图的理解\n",
    "深度学习的主要任务是对模型的构建以及对参数的求解，而这个构建以及计算的逻辑就是计算图。  \n",
    "在本例子中，我们有向前计算的计算图  \n",
    "input * w(权重) + b(偏置)，经过激活函数得到output，並进入下一层神经网络。  \n",
    "后面还定义了反向传播的计算图，包括计算交叉熵损失，利用梯度更新权重等参数。  \n",
    "运行这个计算图，我们就可以在一次次的训练中优化模型，最终得到高精度的模型。  \n",
    "使用计算图的好处在于，我们可以在一个任务中定义多个计算图，从而对比同一组数据在不同算法下的表现；或者在一个算法中输入不同的数据去观察结果的差异，而不需要使用大量的变量和内存去承接算法中间产生的结果。  \n",
    "而它的坏处就在于一开始就要对整个计算流程有一个抽象的理解，比起命令式计算更偏离人的惯性思维，而计算过程中间的变量我们也很难去监控和debug。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 解释这里的模型为什么效果比较差\n",
    "这里的模型效果比较差，只有95%的准确率，因为它只有两层神经网络，並没有隐层。1000 steps的计算相对来说比较少，而且学习率固定在0.3没有变动，使得模型的学习效率交叉。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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